Know what your AI agents do, where they fail and who approves the outcome.
We turn scattered AI use into an inventory of systems, risks, permissions, evaluation evidence, human review paths and practical operating controls.
- AI use inventory
- Risk classification
- Agent evaluations
- Practical control pack
Inventory. Test. Control. Recheck.
- 01InventoryModels, agents, data and owners
- 02EvaluateRepresentative cases and failures
- 03ControlPermissions, policy and approvals
- 04MonitorIncidents, drift and evidence
AI adoption spreads faster than ownership.
Teams use models and agents across content, sales, support, reporting and operations. The risk is not only a wrong answer. It is uncertainty about data access, tool permissions, review responsibility and incident handling.
A useful governance program makes those decisions visible and usable inside daily work. It does not stop at a policy document.
A governance system teams can operate, not shelfware.
The exact scope follows the systems and risk, from a focused agent audit to a broader AI-use governance pack.
Govern the workflow, not the model name alone.
- 01
Set scope
Identify the systems, decisions and people inside the review boundary.
- 02
Collect evidence
Inspect prompts, sources, permissions, tools, logs and representative outputs.
- 03
Test failure
Challenge the agent with ambiguity, missing data, unsafe actions and policy exceptions.
- 04
Close control gaps
Assign owners, add approvals, document policy and define the re-test cadence.
Useful when AI is already in client or operational work.
Governance should match actual risk. A content assistant and a payment-capable agent should not receive the same control model.
Typical triggers
- Clients or procurement ask how AI is used and controlled.
- Agents can access sensitive knowledge or change business systems.
- AI incidents, drift or inconsistent review are becoming visible.
Important boundary
- This is not legal certification or a substitute for qualified legal advice.
- An audit reports evidence and gaps; it does not silently authorize production use.
- Controls must be owned by the client teams that operate the workflow.
What buyers usually ask before scoping the work.
These answers define the normal starting boundary. The project scope follows the workflow, evidence, systems and risk.
Can you audit an agent built by another vendor?
Yes. We review the workflow, knowledge, tools, permissions, evaluation evidence and operating controls independently of the original builder.
Do we need governance before launching a pilot?
The control depth should match the pilot risk, but owners, permissions, human review and failure handling should be explicit before real business actions begin.
What are agent evaluations?
They are representative cases with expected behavior, reject conditions and failure scenarios used to test whether the agent is safe and useful for its intended workflow.
Will we receive a policy?
Where relevant, yes. The policy is paired with practical assets such as an inventory, risk map, AI-use log, approval steps and client-facing explanations.
Tell us which AI system or workflow needs independent review.
Share the agent, business process, users and risk concerns. We will propose a focused audit or governance scope.
Request an AI governance review